System for optimising workflow for efficient on-site data collection and determination of energy analysis and method of operation thereof

ABSTRACT

An energy audit system (1400), including at least one controller (1410) which may obtain attribute information (AI) for an energy consuming space, the AI including information related to a plurality of attributes and corresponding attribute choice information for each of the plurality of attributes; determine dependencies of each of the plurality of attributes; determine a ranking of each of the attributes based upon a dependency score of each of the attributes; determine an optimized workflow based upon the ranking of each of the attributes; and/or renders a user interface (UI) in accordance with the optimized workflow.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is the U.S. National Phase application under 35 U.S.C.§ 371 of International Application No. PCT/IB2014/064758, filed on Sep.23, 2014, which claims the benefit of Provisional Application No.61/881,596, filed on Sep. 24, 2013. These applications are herebyincorporated by reference herein.

The present system relates to a system which can model energyconservation measures (ECMs) for a building and, more particularly, to asystem which can optimize a collection workflow for efficient on-sitedata collection for modeling ECMs and a method of operation thereof.

Reducing energy consumption in buildings represents a major financialopportunity for businesses. The most effective energy retrofits inbuildings from an energy and financial standpoint are lightingretrofits. One of the most critical steps in the process of providingenergy retrofits is the on-site data collection (or audit). In thisstep, an auditor physically walks through the building, from room toroom, and notes details about the room and the installed lighting. Thisprocess today is typically manually performed with pen and paper, anddata collected during the audit must then be re-entered into a softwareprogram for further energy and/or financial analyses. Collecting data bypen and paper has the advantage of being relatively quick at first.However, the overall process is extremely time consuming and inefficientbecause the data has to then be entered into a computer so that it canbe analyzed by, for example, analysis software. FIG. 1 shows a graph 100illustrating a conventional paper audit. Conventional audits may beimproved by using a mobile device during the audit to collect the dataand then to automatically push the data to the analysis software foranalysis (e.g., see, R1). This addresses the time required for the entryof data. However, collecting the data using conventional mobile deviceapplications is in itself time-consuming, especially compared to usingpen and paper. Therefore there is a need for a mobile device applicationthat can collect data in an efficient manner.

FIG. 2 shows a screen shot 200 of a mobile application for performinglighting-based audits. Deficiencies of this mobile application are: (1)A large portion of the user interface uses small buttons that requireprecise touch. This is difficult when walking around with a mobiledevice. (2) The data collection for luminaires, lamps, and ballasts inthis example is all combined into a single line entry for each datafield. Each different luminaire type, lamp type, luminaire dimension,lamp power, etc. will require a separate line item. This results inpotentially thousands of line items, requiring manual scrolling to finda desired line item from the thousands of line items. (3) Other mobileapplications can collect each data field individually (as opposed toaggregating all data fields into a single line entry), but this requiresmany fields of manual data entry, either through small buttons and dropdowns lists, or through manual entry using a soft keyboard or the like,which can be inconvenient and time consuming.

Moreover, although energy conservation measures may be calculated forknown variables, they are typically not determined for uncertainvariables such as fuel cost, weather, etc., which may change in thefuture. For example, energy performance of a building is closely relatedto the long term operating expense associated with it (see, R5). Severaldemonstrations, both in simulation (see, R2, R4) and in practice (R3),have established that one can optimize the energy use of a propertythrough building construction/retrofitting projects. Conventionalstate-of-the-art methods either focus solely on minimizing energyconsumption, while overlooking the financial incentives and occupantcomfort, or aim at optimizing the energy related expenses under oneparticular deterministic setting while omitting stochastic risks, suchas volatility in energy pricing, weather uncertainties, etc., in anoperating environment of a building. Consequently, an owner of abuilding may not find a recommendation for energy saving measures (ECMs)for retrofitting a building (e.g., a construction/recommendation) to beprofitable and/or relevant over its lifetime. Accordingly, asconventional systems which calculate ECS for a building may notcorrectly determine future risks and savings, owners and/or othersassociated with a building may not be willing to undertake projects toimplement the ECMs.

The deficiencies of prior art methods call for an intelligent system fordetermining future costs and/or associated savings due to ECMs which canbe implemented in a building. Accordingly, embodiments of the presentsystem may provide for an intelligent energy retrofit risk managementsystem that can receive fundamental stochastic and/or deterministicrisk/reward factors. Stochastic risk factors refer to the uncertainelements which will affect energy performance and/or comfort of aretrofitted building. Examples include predicted occupancy schedule,future weather conditions, and future energy prices. Deterministic riskfactors refer to the fundamental properties of a building which areknown with certainty and (together with the stochastic risk factors)affect the overall performance of a retrofitted building. Examples ofdeterministic risk factors include physical building properties,lighting composition, and heating, ventilation and air-conditioning(HVAC) system parameters. Using both the stochastic and deterministicrisk factors as inputs, an energy retrofit risk management system inaccordance with embodiments of the present system can build a portfolioof scenarios along with their associated probabilistic view of totalfuture value, which can then be used to optimize energy performance andoccupant comfort (of the building) under uncertainty.

Due to the fluctuations in energy prices and the global warming effectsas a result of pollution emission in the energy generation/conversionprocess, energy conservation has gained much attention recently.Buildings in the United States consume a significant amount of energy.Thus undertaking a building retrofitting project, in which new energyefficient technologies and features are added to an existing building orother structure, can potentially yield both a promisingreturn-on-investment (ROI) due to the future savings obtained byreducing energy consumption and can reduce the negative environmentalimpact due to reduction in greenhouse gas emission such as CO,emissions.

However, as discussed above, not all building owners are interestedand/or motivated to retrofit existing buildings to control and/oroptimize energy use. Two main obstacles to retrofitting existingbuildings are: (1) a lack of clear and convincing financial and/or otherincentives, and (2) the inability of existing models to captureuncertainty in the future—thereby yielding a retrofit solution that maynot be optimal over its lifetime. Hence, to ensure the success of abuilding retrofit project, the owner of the building should be aware ofpotential risk and rewards due to uncertain future energy costs.

The system(s), device(s), method(s), user interface(s), computerprogram(s), processes, etc. (hereinafter each of which will be referredto as system or the system, unless the context indicates otherwise)described herein address problems in prior art systems. Embodiments ofthe present system may detect energy use, predict energy use of astructure such as a building (hereinafter each of which will be commonlyreferred to as building for the sake of simplicity).

Embodiments of the present system may employ an application configuredfor use on a mobile device (MD) such as a smart phone, a tablet, etc.,and may be operative to collect data during a lighting-based audit.Embodiments of the present system may optimize workflow so as toefficiently collect data of the lighting-based audit. The efficiency ofdata collection may be achieved through, for example, methods whichemploy: 1) a hierarchical data collection structure to reduce manualdata entry and/or user selections, and 2) a user interface (UI) thatoptimizes the efficiency and/or convenience of the interaction between auser such as an auditor and the MD.

In accordance with embodiments of the present system, there is disclosedan energy audit system (1400), including at least one controller (1410)which may obtain attribute information (AI) for an energy consumingspace, the AI including information related to a plurality of attributesand corresponding attribute choice information for each of the pluralityof attributes; determine dependencies of each of the plurality ofattributes; determine a ranking of each of the attributes based upon adependency score of each of the attributes; determine an optimizedworkflow based upon the ranking of each of the attributes; and/orrenders a user interface (UI) in accordance with the optimized workflow.

It is also envisioned that the controller may further obtain attributechoice information selected by a user and which corresponds to at leastone of the plurality of attributes. Moreover, the controller may furtherdetermine the dependencies of each of the plurality of attributes basedupon a dependency matrix. Further, the controller may determine theranking based upon the determined dependencies of each of the pluralityof attributes. It is also envisioned that the controller may determinewhether an attribute of the plurality of attributes is a dependentattribute based upon the ranking of each of the plurality of attributes.It is further envisioned that the controller may automatically populateattribute choice information corresponding to at least one of theplurality of attributes that is determined to be a dependent attribute.

In accordance with yet other embodiments of the present system, there isprovided a method of generating an energy audit, the method may beperformed by at least one controller and may include one or more acts ofacts of: obtaining attribute information (AI) for an energy consumingspace the AI including information related to a plurality of attributesand corresponding attribute choice information for each of the pluralityof attributes; determining dependencies of each of the plurality ofattributes; determining a ranking of each of the attributes based upon adependency score of each of the attributes; determining an optimizedworkflow based upon the ranking of each of the attributes; and renderinga user interface (UI) in accordance with the optimized workflow.

It is also envisioned that the method may further include an act ofobtaining attribute choice information selected by a user and which maycorrespond to at least one of the plurality of attributes. Moreover, themethod may include an act of determining the dependencies of each of theplurality of attributes based upon a dependency matrix. Further, themethod may include an act of determining the ranking based upon thedetermined dependencies of each of the plurality of attributes. It isalso envisioned that the method may include an act of determiningwhether an attribute of the plurality of attributes is a dependentattribute based upon the ranking of each of the plurality of attributes.It is also envisioned that the method may include an act ofautomatically populating attribute choice information corresponding toat least one of the plurality of attributes that is determined to be adependent attribute.

In accordance with yet further embodiments of the present system, thereis disclosed a computer program stored on a computer readable memorymedium, the computer program configured to generate informationindicative of an energy audit for a space, the computer program mayinclude a program portion which may be configured to: obtain attributeinformation (AI) for an energy consuming space the AI includinginformation related to a plurality of attributes and correspondingattribute choice information for each of the plurality of attributes;determine dependencies of each of the plurality of attributes; determinea ranking of each of the attributes based upon a dependency score ofeach of the attributes; determine an optimized workflow based upon theranking of each of the attributes; and/or render a user interface (UI)in accordance with the optimized workflow on a display of the system.

It is also envisioned that the program portion may be further configuredto obtain attribute choice information selected by a user and whichcorresponds to at least one of the plurality of attributes. Further, theprogram portion may be further configured to determine the dependenciesof each of the plurality of attributes based upon a dependency matrix.Moreover, the program portion may be further configured to determine theranking based upon the determined dependencies of each of the pluralityof attributes. It is also envisioned that the program portion may befurther configured to determine whether an attribute of the plurality ofattributes is a dependent attribute based upon the ranking of each ofthe plurality of attributes. It is also envisioned that the programportion may be further configured to automatically populate attributechoice information corresponding to at least one of the plurality ofattributes that is determined to be a dependent attribute.

The invention is explained in further detail, and by way of example,with reference to the accompanying drawings wherein:

FIG. 1 shows a graph illustrating a conventional paper audit;

FIG. 2 shows a screen shot of a mobile application for performinglighting-based audits;

FIG. 3 shows a flow diagram that illustrates a process performed by anenergy analysis system in accordance with embodiments of the presentsystem;

FIG. 4 shows a screen shot of an optimized workflow implemented on amobile device in accordance with embodiments of the present system;

FIG. 5 shows a screen shot of an optimized workflow implemented on amobile device in accordance with embodiments of the present system;

FIG. 6 shows a screen shot of an optimized workflow implemented on amobile device in accordance with embodiments of the present system;

FIG. 7 shows a screen shot of an optimized workflow implemented on amobile device in accordance with embodiments of the present system;

FIG. 8 shows a screen shot of a populated menu formed using an optimizedworkflow implemented on a mobile device in accordance with embodimentsof the present system;

FIG. 9 shows a flow diagram that illustrates a process performed by anenergy analysis system including a risk management engine (RME) inaccordance with embodiments of the present system;

FIG. 10 shows a graph illustrating use patterns for five differentcategories of buildings in accordance with embodiments of the presentsystem;

FIG. 11 shows a graph of retrofit variables to a risk management engine(RME) operating in accordance with embodiments of the present system;

FIG. 12A shows a graph of risk factors of a client generated inaccordance with embodiments of the present system;

FIG. 12B shows a graph illustrating energy savings of solutionsgenerated in accordance with embodiments of the present system;

FIG. 13 shows a graph of retrofitted office operating cost against pricescenarios generated in accordance with embodiments of the presentsystem; and

FIG. 14 shows a portion of a system in accordance with embodiments ofthe present system.

The following are descriptions of illustrative embodiments that whentaken in conjunction with the following drawings will demonstrate theabove noted features and advantages, as well as further ones. In thefollowing description, for purposes of explanation rather thanlimitation, illustrative details are set forth such as architecture,interfaces, techniques, element attributes, etc. However, it will beapparent to those of ordinary skill in the art that other embodimentsthat depart from these details would still be understood to be withinthe scope of the appended claims. Moreover, for the purpose of clarity,detailed descriptions of well known devices, circuits, tools, techniquesand methods are omitted so as not to obscure the description of thepresent system. It should be expressly understood that the drawings areincluded for illustrative purposes and do not represent the entire scopeof the present system. In the accompanying drawings, like referencenumbers in different drawings may designate similar elements.

FIG. 3 shows a flow diagram that illustrates a process 300 performed byan energy analysis system in accordance with embodiments of the presentsystem. The process may determine a workflow that may be used to acquirebuilding information (BI) for a space. The space may include any spacewhich may consume energy such as a space in a building (e.g., a room, ahallway, a storage area, etc), a structure, a stadium, an outdoor area(e.g., a park, a street, a walkway, a highway, a road, etc.). Further,for the sake of simplicity of the present description, the term buildingmay refer to a building, a structure, a stadium, an outdoor area (e.g.,a park, a street, a walkway, a highway, a road, etc.), or any other areain which the present system may be suitably employed. The buildinginformation (BI) may be used to determine the use and/or energyconsumption for a space of the building so as to predict energyconservation measures (ECMs) in accordance with embodiments of thepresent system. The process 300 may be performed using one or morecomputers communicating over a network and may obtain information and/orstore information using one or more memories which may be local and/orremote from each other. The process 300 can include one of more of thefollowing acts. Further, one or more of these acts may be combinedand/or separated into sub-acts, if desired. Further, one or more of theacts of the process 300 may be performed sequentially or in parallelwith one or more other acts of the process 300. In operation, theprocess may start during act 301 and then proceed to act 303.

During act 303, the process may obtain attribute information (AI). Theattribute information and may include a plurality of attributes. Forexample, for a given list of n attributes, the attributes may berepresented as a₁ through a_(n) (a₁-a_(n)) and may be formed in vectorform (e.g., row and/or column vector form), if desired. In the presentexample, it will be assumed that the attribute information (AI) isobtained from a memory of the system and may include a default settingsuch as shown in Table 3 below.

In accordance with embodiments of the present system, this act may beperformed once to initialize embodiments of the present system andresults may be stored in a memory of the system for later use. Forexample, this act may be performed to obtain data that may be used tostructure the data collection as efficiently as possible for a specificapplication (e.g., across different buildings that are of the samesurvey type such as hospital buildings, etc. and users of this processmay not have to repeat this act). For another application (e.g., abuilding of a different survey type such as an office building, etc.instead of the hospital discussed above), this act may be performedinitially or repeatedly for each different survey type or as desired.After completing act 303, the process may continue to act 305.

During act 305, the process may determine dependencies for each of theattributes a₁-a_(n). The process may perform this act by, for example,constructing a dependency matrix D where each row and column correspondsto an attribute. Thus the dependency matrix (D) is of dimension n×n andmay be represented as:

$D = {\begin{matrix}\begin{matrix}a_{1} \\\vdots\end{matrix} \\a_{n}\end{matrix}\overset{\begin{matrix}a_{1} & \ldots & a_{n}\end{matrix}}{\begin{bmatrix}\; & \ldots & \; \\\vdots & \ddots & \vdots \\\; & \ldots & \;\end{bmatrix}}}$

Each row of D is then a row vector indicating dependencies for theattribute corresponding to that row. For a simple example, given threeattributes a₁,a₂,a₃;

$D = \begin{bmatrix}0 & 1 & 1 \\0 & 1 & 0 \\1 & 1 & 1\end{bmatrix}$

The first row of D may indicate the dependencies for a₁ (where adependency is only indicated by a 1 (as opposed to a 0)) the second rowof D may indicate the dependencies for a₂; and the third row of D mayindicate the dependencies for a₂. For this example then, thedependencies may be represented as shown in Table 1 below.

TABLE 1 Attribute Dependencies a₁ a₂, a₃ a₂ a₂ a₃ a₁, a₂, a₃

For example, for four attributes: 1) Luminaire Lamp Type, 2) LuminaireType, 3) Luminaire Dimensions, and 4) Lamp Subtype, Luminaire Lamp Typehas the highest dependency score since the highest number of attributeshave a dependency on the Luminaire Lamp Type (e.g., see Table 4 below).In this way, the choice of Luminaire Lamp Type (e.g., linear fluorescentvs. incandescent) may be used to determine what possible Luminaire Typesare applicable, which in turn may be used to determine what LuminaireDimensions are applicable, which in turn determines what Lamp Subtypesmay be applicable. After completing act 305, the process may continue toact 307.

During act 307, the process may determine a dependency score (S) foreach attribute a₁-a_(n) (e.g., a₁,a₂,a₃, in the present example) as alinear combination (summation) of the dependencies. Thus a summation ofthe elements in each column of D will give a dependency score for eachattribute a₁-a_(n). A vector of dependency scores

(i.e., a dependency score vector) may be calculated as a linearcombination (summation) of the columns in D. For a simple summation, Dis multiplied by a row vector

of ones of dimension n.

=

D

Continuing the above-described example, then,

${\overset{\rightharpoonup}{S} = {\begin{bmatrix}1 & 1 & 1\end{bmatrix}\begin{bmatrix}0 & 1 & 1 \\0 & 1 & 0 \\1 & 1 & 1\end{bmatrix}}},$

and

=[1 3 2]

Thus, the dependency score for each of the attribute a₁-a_(n) (e.g.,a₁,a₂,a₃, in the present example) may be represented as shown in Table 2below.

TABLE 2 Attribute Dependency Score a₁ 1 a₂ 3 a₃ 2

After completing act 307, the process may continue to act 309.

During act 309, the process may determine a ranking for each of theattributes a₁-a_(n) (e.g., a₁,a₂,a₃, in the present example) based uponthe corresponding attributes dependency score. Thus, the process mayrank attributes with the highest determined dependency scores highest;and attributes with the lowest determined dependency scores lowest.

However, in yet other embodiments, it is envisioned that the process mayrank attributes with the highest determined dependency scores lowest andattributes with the lowest determined dependency scores highest, ifdesired. After completing act 309, the process may continue to act 311.

During act 311, the process may form a hierarchical data collectionstructure in accordance with a ranking of each of the attributes. Forexample, the highest ranking attributes may be positioned highest in thehierarchical data collection structure and lowest ranking attributes maybe positioned lowest in the hierarchical data collection structure.Thus, the process may form a data collection structure based upon theranking (score) of one or more corresponding attributes. Thehierarchical data collection structure may be considered an optimizedworkflow. The process may further compare the ranking of each of theattributes with a threshold value, and if it is determined that rankingof an attribute of the attributes is greater than or equal to thethreshold value, the process may position the attribute as discussedabove in the hierarchical data collection structure. However, if it isdetermined that ranking of the attribute is less than the thresholdvalue, the process may select a default value (e.g., 0) for theattribute. After completing act 311, the process may continue to act313.

During act 313, the process may render the optimized workflow on a userinterface (UI) of the system such as on a display of a device. Theoptimized workflow may include one or more menus generated by the systemwhich may include menu items for selection by the user. The menu itemsmay include, for example, attributes and one or more correspondingchoices which may be selected by the user. After completing act 313, theprocess may continue to act 315.

During act 315, the process may obtain selections of the attributechoices selected by, for example, the user. Accordingly, the process maymonitor selection by a user (e.g., user selection) of the menu itemscorresponding to the attribute choices. After completing act 315, theprocess may continue to act 317.

During act 317, the process may determine one or more energy savingsmeasures (ESMs) based upon the selection of the attribute choices by theuser. The ESMs may be determined for example, by the process using anysuitable application or applications.

For example, in accordance with embodiments of the present system, ESMsmay be related to lighting, so lighting retrofits (e.g., energyefficient lighting replacements), and controls (e.g., occupancy-basedswitching, daylight dimming) may be used. The ESMs may be determinedautomatically or by a user (e.g., a salesperson) with sufficientknowledge/skills. This user may assess a current situation of energy useby examining the data collected as a result of the building audit, andthen recommend, for example, energy saving measures such as a lampreplacement, or controls, etc. based on the expertise of the user, inorder to conserve energy while providing an enhanced visual environmentdue to installed energy savings measures (e.g., light dimming, lamptype, etc.). Data collected by embodiments of the present system (e.g.,an audit tool may also be used for an energy retrofit risk managementsystem that may further analyze ESMs. In yet further embodiments, acomputer or tablet-based tool may use the audit data in order to selectand recommend appropriate ESMs. After completing act 317, the processmay continue to act 319.

During act 319, the process may render results of the process such asthe ESMs on a UI of the system such as a display for the convenience ofthe user. The UI may further include one or more selections and/orparameters which may be selected and/or changed by the user so that auser may fine tune results of the process, if desired. For example, theuser may modify one of the choices (e.g., see, act 313) and the processmay update the ESMs in real time in accordance with the user'smodification. After completing act 319, the process may continue to act321.

During act 321, the process may form and/or update history informationin accordance with results of the process and/or any user selections(e.g., during act 315). The history information may be stored in amemory of the system for later use. After completing act 321, theprocess may continue to act 323, where it ends.

EXAMPLE

The above-described process will now be described with reference to anexample where n=12. Further, because it is assumed that about 90% of alllighting retrofits involve linear fluorescent lamps, the detaileddescription described here will focus on linear fluorescent lamps. Thefollowing attributes and corresponding choices will be assumed as listedin Table 3 below.

TABLE 3 No. Attribute (ATTRIBUTE) Choice(s) 1 Luminaire Lamp TypeFluorescent, CFL, HID 2 Luminaire Type Troffer, Strip 3 LuminaireDimensions 1 × 4, 2 × 2 4 Number of Luminaires User Input 5 Lamp SubtypeT12, T8 6 Lamp Wattage 32 W, 28 W 7 Lamp Color Temperature 3000 K, 4500K 8 Number of Lamps Per Luminaire 2, 3, 4 9 Ballast TypeElectromagnetic, Electronic 10 Ballast Factor Low, Normal, High 11Ballast Input Voltage 120 V, 277 V 12 Number of Ballasts Per Luminaire1, 2

As n=12, there are a total of twelve attributes. Accordingly, theprocess may form a dependency matrix (D) as follows:

$D = \begin{bmatrix}0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0\end{bmatrix}$

Then, a vector of dependency scores

may be calculated as:

$\overset{\rightharpoonup}{S} = {\begin{bmatrix}1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1\end{bmatrix}{\quad{\begin{bmatrix}0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0\end{bmatrix},}}}$where,

=[5 4 4 0 2 0 0 0 0 0 0]

Thus, the dependency score for each of the attributes a₁-a₁₂ may berepresented as shown in Table 4 below. All other dependency scores lowerthan four may be set to a default value such as 0.

TABLE 4 Attribute Dependency Score Rank Luminaire Lamp Type 5 1Luminaire Type 4 2 Luminaire Dimensions 4 2 Number of Luminaires 0 LampSubtype 2 3 Lamp Wattage 0 Lamp Color Temperature 0 Number of Lamps PerLuminaire 1 4 Ballast Type 0 Ballast Factor 0 Ballast Input Voltage 0Number of Ballasts Per Luminaire 0

Thus the top four attributes by dependency score are: Luminaire LampType, Luminaire Type, Luminaire Dimensions, and Lamp Subtype,respectively. Accordingly, these attributes are ranked in order fromfirst through fourth as shown. Accordingly, the hierarchical datacollection structure in accordance with embodiments of the presentsystem may include these top four attributes by ranking. Aninterpretation of the hierarchy of each attribute and correspondingchoices (e.g. for a user's selection) is listed in Table 5 below.

TABLE 5 Rank Attribute Choices Notes 1 Luminaire Lamp e.g. FluorescentThe choice of Luminaire Lamp Type will Type Linear, determine thepossible Luminaire Types. Fluorescent Compact, HID, Incandescent, LED 2Luminaire Type e.g. for The choice of Luminaire Type will determineFluorescent the possible Luminaire Dimensions Linear, Commercial Strip,High Bay, Troffer, etc. 3 Luminaire e.g. for Troffer, 1 × The choice ofLuminaire Dimensions will Dimensions 4, 2 × 2, 2 × 4, determine thepossible Lamp Subtypes etc. 4 Lamp Subtype e.g.. for 1 × 4, T12, Thechoice of the Lamp Subtype will allow T8, T5, etc. the rest of the datafields to be populated with common default values, as well as possibleoptions. Luminaire dep./see notes The choice of Luminaire Dimension inDimension combination with the choice of Lamp Subtype, will determinethe possible options for Lamp Power. The Luminaire Dimension determinesthe lamp length, which in combination with the Lamp Subtype determinespossible Lamp Power . . . e.g. a 1 × 4 Luminaire can accept a 4 ft T12lamp, which can have a Standard power of 40 W or an energy saving powerlevel of 34 W. 40 W can be presented as the default Lamp Power. Lampcolor dep./see notes Lamp color temperature is largely temperatureindependent of all other parameters and can be set to a default valuethat is common. The Number of dep./see notes The Number of Lamps PerLuminaire is Lamps Per determined by the Luminaire Dimensions . . .Luminaire e.g. a 1 × 4 luminaire will accept 1, 2, 3, or 4 lamps, with 2lamps being common. Ballast Type dep./see notes For Ballast Type, thechoice of Electromagnetic, Electronic Instant Start, or Electronic RapidStart can default based on choice of Luminaire Type and Lamp Subtype(e.g. T5 and T8 lamps should always have Electronic Ballasts, whereInstant or Rapid start can be inferred from Luminaire Type, e.g. HighBay in warehouse applications are typically Instant Start). BallastFactor dep./see notes The Ballast Factor can always default to Normal,and then be adjusted by a user such as an auditor if necessary to Highor Low. Ballast Input dep./see notes The Ballast Input Voltage defaultsto the Voltage project input voltage as entered in the application.Number of dep./see notes The Number of Ballasts Per Luminaire BallastsPer depends on the Luminaire Dimension and Luminaire Number of Lamps PerLuminaire. In most cases a default value of one is sufficient

Accordingly, the optimized workflow has four attribute selections: (1)Luminaire Lamp Type, (2) Luminaire Type, (3) Luminaire Dimension, and(4) Lamp Subtype, in order (e.g., by ranking) The choices selected bythe user for these four attribute selections may then be used by thesystem to automatically determine choices for one or more of the otherattributes (e.g., marked “dep” for dependent). Thus, based upon an entryof an attribute selection, further depent attribute selections may beprovided. Thus, in accordance with embodiments of the present system aselection process may be performed using expert domain knowledge, forexample designed into the system, which may make attribute selectionsbased upon known attribute selections. For example, embodiments of thepresent system may use expect domain knowledge to determine that aLinear Fluorescent can have Luminaire Type Troffer, which, in turn, mayhave dimensions of 1×4, 2×2, etc. Thus, the process may automaticallyselect known attribute selections (e.g., Luminaire Type Troffer) and mayprovide to a user a menu with (further) available attribute selectionsbased upon previous attribute selections thereby greatly simplifyingentry choices available for selection by a user.

The process may then render the optimized workflow on a user interface(UI) of the system such as a display. As only four attribute selectionsare selected from a total of 12 (e.g., n=12), a process of entering userselections is simplified. The process may form a menu including thesefour attribute selections and corresponding attribute choices. Thus,from a user interface (UI) perspective, the four attribute selections inthe optimized workflow may be rendered using any suitable method such asgraphics, and/or button-based menu-item inputs. Thus, a user such as anauditor may collect a majority of required information (e.g., for 12attributes) using only four clicks for the luminaire/lamp/ballast inaccordance with embodiments of the present system. The menu-item inputsof the present system have the following advantages: (a) The graphicalinput provides the user with a large selection area (e.g., menu items,check boxes, or buttons, etc.) to utilize when making a selection, whichcan eliminate user entry inefficiencies which may occur when using asmall selection area (e.g., small menu items or buttons) which isdifficult to select when in a mobile environment; (b) a menu-item (e.g.,including graphics, buttons, etc.) based entry methods may enable fasteridentification by the user of the appropriate option compared to using adrop down list entry area; and (c) a hierarchical data collectionstructure in accordance with embodiments of the present system assuresthat the user is never presented with “too many” options at any onetime.

Several screenshots illustrating operation of the above-describedexample with n=12 will now be described below with reference to FIGS. 4to 8. FIGS. 4 through 8 are shown in an actual order generated by thesystem and correspond with the ranking of the attributes (e.g. by levelstarting at the first level). In accordance with embodiments of thepresent system, the highest ranked attribute is assigned to a firstlevel screen (e.g., 402) and the next highest ranked attribute isassigned a second screen level (e.g., 502) However, other orders arealso envisioned.

FIG. 4 shows a screen shot 400 of an optimized workflow implemented on amobile device in accordance with embodiments of the present system. Theprocess may generate and/or display at least one menu such as menus 401,402. Menus may be considered a first-order (or level) hierarchical datacollection structure (e.g., corresponding with ranking) With regard tomenu 402, this menu may correspond with the highest ranking attribute(e.g., see Table 5) and may include selection items 404A through 404E(generally 404-x) for each corresponding attribute choice. A back button406 may be provided to return to a previous window, screen, etc., whenselected. Menu 401 is an underlying menu that is populated using anoptimized workflow implemented on a mobile device in accordance withembodiments of the present system.

FIG. 5 shows a screen shot 500 of an optimized workflow implemented on amobile device in accordance with embodiments of the present system. Theprocess may generate and/or display at least one menu such as menus 501,502. Menu 502 may be considered a second-order hierarchical datacollection structure (e.g., corresponding with ranking). With regard tomenu 502, this menu may correspond with the second highest rankingattribute (e.g., see Table 5) and may include selection items 504Athrough 504I (generally 504-x) for each corresponding attribute choice.Selection of one or more of the selection items 504-x may be utilized topopulate the menu 501 with information related to the Luminaire Detailssimilarly as discussed with regard to the menu 401. A back button 506may be provided to return to a previous window, screen, etc., whenselected.

FIG. 6 shows a screen shot 600 of an optimized workflow implemented on amobile device in accordance with embodiments of the present system. Theprocess may generate and/or display least one menu such as menus 601,602. Menu 602 may be considered a third-order (level) hierarchical datacollection structure (e.g., corresponding with ranking) With regard tomenu 602, this menu may correspond with the third highest rankingattribute (e.g., see Table 5) and may include selection items 604Athrough 604I (generally 604-x) for each corresponding attribute choice.Selection of one or more of the selection items 604-x may be utilized topopulate the menu 601 with information related to the LuminaireDimensions similarly as discussed with regard to the menu 401. A backbutton 606 may be provided to return to a previous window, screen, etc.,when selected.

FIG. 7 shows a screen shot 700 of an optimized workflow implemented on amobile device in accordance with embodiments of the present system. Theprocess may generate and/or display least one menu such as menus 701,702. Menu 702 may be considered a fourth-order (level) hierarchical datacollection structure (e.g., corresponding with ranking) With regard tothe menu 702, this menu may correspond with the fourth highest rankingattribute (e.g., Table 5) and may include a selection items 704A through704I (generally 704-x) for each corresponding attribute choice.Selection of one or more of the selection items 704-x may be utilized topopulate the menu 701 with information related to the Lamp Subtypessimilarly as discussed with regard to the menu 401. A back button 706may be provided to return to a previous window, screen, etc., whenselected.

FIG. 8 shows a screen shot 800 of a populated menu 801 formed using anoptimized workflow implemented on a mobile device in accordance withembodiments of the present system. The menu 801 is populated inaccordance with selections entered using the workflow in accordance withembodiments of the present system for example through interaction withthe menus 402, 502, 602, 702 as discussed above. In accordance withembodiments of the present system, the user may select attribute choicesfor the four highest-ranking attributes and the process may populate theattribute choices for the other eight attribute choices (for example ofthe twelve attribute choices in the current emboidments) in accordancewith the user-entered selections. Some remaining attributes may have nodependencies based on previous selections, accordingly, these items maybe left blank, or assigned (common) default values, as may be desired inaccordance with system settings.

Thus, embodiments of the present system provide an optimized workflowfor efficient on-site data collection without a need for manual entry ofall of the data items. The optimized workflow may include a hierarchicaldata collection structure that may provide for algorithmic reduction ofmanual data entry. Further, the optimized workflow may provide for thereduction of subsequent choices presented to the user with each entry ofdata. Moreover, it is envisioned that the hierarchical data collectionstructure may be determined through a dependency scoring method. It isfurther envisioned that the dependency scoring may include a dependencymatrix whereby dependency scores may be calculated from a linearcombination of the elements in each column of the dependency matrix. Itis also envisioned that the attributes with highest dependency scoresmay be assigned to the top of the hierarchical data collectionstructure. Further, in accordance with embodiments of the presentsystem, the optimized workflow may provide for a user interface (UI)that may optimize an interaction between a user (e.g., an energyauditor) and the MD. It is also envisioned that the optimized workflowin accordance with embodiments of the present system may reduce a numberof variables (e.g., attributes and/or choices) to be presented to,and/or selected by, the user, thus enabling an efficient user interface(UI). It is also envisioned that the UI may include a large graphicaltype menu including large menu selection items which do not requireprecise user movement for selection.

Uncertainties and ECMs

In accordance with yet other embodiments of the present system, anenergy retrofit system including a risk management engine (RME) thatreceives fundamental stochastic and/or deterministic risk and/or rewardfactors as inputs may be utilized. Then, using these inputs, the RME maybuild one or more energy use models along with their associatedprobabilistic total future costs/value. For example, a relationshipbetween the consumption of energy (e.g., of a selected fuel such aselectricity, fuel oil, natural gas, steam, etc.), a human comfort level,and/or various retrofitting parameters (e.g., focusing on lightingrenovation, HVAC tuning, etc.) based upon one or more of weather and/orenergy price (e.g., for one or more selected sources) uncertainties maybe determined. Specifically, the RME may employ stochastic models whichemploy retrofitting parameters to predict key building performancemetrics, examples of which may include energy consumption (e.g.,electricity consumption, natural gas consumption, etc.) and/or expectedoccupant comfort. The RME may optimize over retrofitting parameters withrespect to the various stochastic risk factors (e.g., energy priceand/or future weather) which may be uncertain. The RME may output a listof retrofit recommendations (e.g., a list of energy conservationmeasures (ECMs)), such as lighting and HVAC recommendations, whichenhance one or more of net present value, occupant comfort level, etc.These ECMs may be more robust to future uncertainties such as energyand/or weather uncertainties than ECMs produced by conventional methods.Moreover, the RME may be applied to different categories of buildings(e.g., offices, hospitals, factories, warehouses, and schools) as may bedesired.

FIG. 9 shows a flow diagram that illustrates a process 900 performed byan energy analysis system including an RME in accordance withembodiments of the present system. The process may determine a workflowthat may be used to acquire building information (BI) for one of morespaces of a building. The building information may be used to determinethe use and/or energy consumption for the space so as to assist inselecting energy conservation measures (ECMs) in accordance withembodiments of the present system. The process 900 may be performedusing one or more computers communicating over a network and may obtaininformation and/or store information using one or more memories whichmay be local and/or remote from each other. The process 900 can includeone of more of the following acts. Further, one or more of these actsmay be combined and/or separated into sub-acts, if desired. Further, oneor more of the acts of the process 900 may be performed sequentially orin parallel with one or more other acts of the process 900. Moreover,the process 900 may include first through third blocks 902, 904, and906, respectively, shown in column form. The first block 902 illustratesinputs into the energy analysis system. The second block 904 includesacts performed by the RME operating in accordance with embodiments ofthe present system. IN accordance with embodiments of the presentsystem, the third block 906 represents outputs of the RME.Illustratively, the process 900 may start at act 903.

The process 900 may include three main acts each of which may includeone or more subacts. For example, the process 900 may include input actsof the first block 902, risk management decision acts performed by theRME of the second block 904, and output acts of the third block 906.

With regard to the input acts 902, information obtained during the inputacts may be obtained using any suitable method such as hierarchicalinput methods as discussed elsewhere in this application, direct userinputs, and/or may be retrieved from a memory of the system. Further,the process may generate and/or render a user interface (UI) which mayinclude one or more menus with which a user may interact to enterdesired information. Generally, in accordance with some embodiments,acts 903-915 may be performed as follows: act 903 may be performed toobtain information related to building type (e.g., hospital, etc.),geographic location of the building, the orientation of the building,the shape of the building, the size of the building, the size andorientation of the windows, roof structure of the building, the HVACsystem, etc. Act 905 may be performed to obtain information related toweather condition, use pattern variations and/or energy prices, etc.Acts 907 and 909 may be performed to obtain retrofit variables underconsideration (e.g., installed lighting types, thermostat setpointschedules, lighting controls), which are the variables to be optimizedover (e.g., minimize electricity and natural gas consumption), and thefixed/promised return of the retrofit project (e.g., energy costsavings, return on investment, etc.). Acts 911 and 913 may be performedto obtain information related to expected return and risk tolerancelevels for financial, environmental and other measures. Act 915 may beperformed to obtain information related to existing knowledge aboutenergy prices, material costs, technology efficiency levels, weatherforecasts. These acts will be discussed below in further detail.

During act 903 the process may obtain information related to a buildingsuch as building property information (BPI). The BPI may includeinformation related to building type, energy consumption, pollutionemission levels, occupant comfort levels and/or other buildingperformance metrics and may be obtained as attribute information (AI)which may be entered by a user and/or obtained from a memory of thesystem. In some embodiments, the AI may include default information suchas based on an identified building type. If desired, the AI may beobtained as discussed elsewhere in this application. Moreover, the BPImay include information related to the building such as a geographiclocation of the building, an orientation of the building, a shape of thebuilding (e.g., square, round, oval, rectangular, flat roof, A-frame,etc.), the size of the building (e.g., in meters²), size of windows(e.g., in meters²) of the building, and orientation of the windows(e.g., facing north, south, east, west, etc.), roof structure of thebuilding (e.g., flat, gable, insulated, un-insulated, etc.), heatingventilation and air conditioning (HVAC) parameters and type, and/orother characteristics of the building. Further, the information relatedto the BPI may include information such as energy consumption, pollutionemission levels, occupant comfort levels and/or other buildingperformance metrics for the building. Generally, the BPI may beconsidered deterministic information.

During act 905, the process may obtain information related touncertainty information (UI) such as weather information, use patterninformation (e.g., occupancy information), energy price informationand/or other information uncertainty variables. The system and/or usermay decide which of the weather information, the use patterninformation, the energy price information and/or the other uncertaintyvariables to acquire. Moreover, one or more of the weather information,the use pattern information, the energy price information and/or theother uncertainty variables may be weighted so that an importance may beadjusted relative to other of the weather information, the use patterninformation, the energy price information and/or the other uncertaintyvariables.

With regard to the weather information, the process may model future(e.g., predicted) weather patterns (e.g., to capture uncertainty infuture weather conditions) using any suitable method. For example, theprocess may obtain actual weather information for a period of time(e.g., a past time period) and use this information to model (e.g.,predict) future weather. Accordingly, the process may obtain actualweather information which, for example, may include (e.g., hourly, etc.)weather information of an area corresponding to a particular location ofthe building for the time period. This weather information may include(e.g., hourly) information related to, for example, temperature,daylight, humidity, wind speed, etc., collected over the time period.The time period may be set by the system and/or user to any value (e.g.,1 day, 1 month, 1 year, 10 years, etc.).

Then, to model future weather patterns, the process may randomlygenerate weather information for a number of time periods (e.g., thirtydays in the current example) in which hourly temperature readings aregenerated in based upon the actual weather information and randominformation (e.g., random values) generated by the process. For example,each of the predicted (hourly) temperatures is generated by anindependent normal variable centered around a typical hourly readingwith standard deviation of 3 degrees. The value 3 is selected based onempirical studies. However, the standard deviation may include othervalues, if desired.

Further, with regard to the use pattern information (e.g., occupancyinformation), this information may provide information relevant todetermine heat generated such as by building occupants, lighting use,etc. and comfort based for example on thermostat settings duringoccupancy demanded by the occupants of the building. The comfortdemanded by occupants in the building may be represented as a percentageof occupants who are comfortable with a certain interior climate (e.g.,temperature, humidity, etc.) such as 75% of the occupants.

Further, the use pattern may be based upon the building's category(e.g., type). For example, FIG. 10 shows a graph 1000 illustrating usepatterns and corresponding use pattern information for five differentcategories of buildings in accordance with embodiments of the presentsystem. For example, with respect to an office category, the work hours(e.g., use patterns) are Mon-Fri 8:00 am to 5:00 pm and Sat 9:00 am to2:00 pm. Referring to graph 1000, illustrative hourly use schedules forfive categories of buildings are shown. These categories include anoffice, a school, a hospital, a warehouse and a two-shift factory.However, in other embodiments, other categories and/or hours may beused. Embodiments of the present system may include an application tosimulate energy use of a building using a tool such as a simulation toolknown as EnergyPlus™ and which is discussed below in further detail. TheEnergyPlus™ application may include detailed simulation features foroccupancy of a building for any day, date, time. Accordingly, theEnergyPlus™ application may track a schedule of the building by day,date, time, etc. Further, the EnergyPlus™ application may keep track ofholidays for a particular year so a more refined simulation may beprovided.

Referring back to FIG. 9, during act 907, the process may obtaininformation related to contract information. The contract informationmay include information which may characterize one or more of theretrofit variables (as will be discussed with regard to act 909 below)under consideration which are the variables to be optimized over, andthe fixed/promised return of the retrofit project. The contractinformation may further include (reward/penalty) terms regarding theuncertain factors which might affect the return.

During act 909, the process may obtain information related to theretrofit variables and determine information such as heat information(e.g., see, FIG. 11, element 1116), lighting power density (LPD)information (see, FIG. 11, element 1118), and schedule information (see,FIG. 11, element 1120). The information related to the retrofitvariables are illustratively shown in FIG. 11 as a graph of retrofitvariables to be processed to obtain information to be input by an RME1122 operating in accordance with embodiments of the present system.Although exemplary retrofit variables are shown and described, it isenvisioned that other retrofit variables may also be provided and/orused by the process, if desired.

Referring to FIG. 11, retrofit variables may include information such ashours of daylight 1102, such as may be provided by daylight sensors,schedules of sunrise/sunset, etc., may be utilized together withbuilding work hours 1104 to determine a daylight control profile 1112(e.g., light dimming control). Building usage patterns 1106 may bemanually input and/or may be provided by occupancy sensors 1114 and maybe utilized together with the work hours 1104 for determining a lightingschedule of the building 1120 (e.g., a lighting schedule ofon/off/dimming for building lighting). Temperature set points 1108 maybe utilized to determine heating requirements 1116 such as in watts persquare foot. Lighting fixture composition 1110, and the daylight control(dimming) profile 1112 may be utilized to determine the lighting powerdensity (LPD) information 1118 using any suitable method such as using asimulation software such as SPOT™ (Sensor Placement+Optimization Tool)and/or CalcZone™. Thereafter, the heat information 1116, the LPDinformation 1118, and the schedule information 1120 may be input intothe RME 1122 for further processing to produce a meta model 1124 whichmay include information related to recommended retrofit variables. Inaccordance with embodiments of the present system, the meta model may,for example, describe electricity consumption as a function of LPD andcontrol options, or electricity and natural gas consumption as afunction of heating and cooling setpoints. However, in yet otherembodiments, other meta models may be defined by, for example, thesystem and/or user.

Referring back to FIG. 9, during act 911, the process may obtaininformation related to provider goal(s). The information related toprovider goals may be specified both in terms of expected return andrisk tolerance levels for financial, environmental and other measures.For example, the provider goals may be to deliver a project with 20-30%profit margin with 75-90% certainty. During act 913, the process mayobtain information related to client goal(s) (e.g. to realize 20-30%return on investment with 80% certainty). The information related toclient goals may be specified both in terms of expected return and risktolerance levels for financial, environmental and other measures.

Information obtained and/or generated during any of the acts of theprocess 900 may be stored in a memory of the system for later use ashistorical information in, for example, a historical data database 915.The historical data may include information related to existingknowledge (e.g., deterministic information) energy prices, materialcosts, technology efficiency levels (e.g., lighting efficiency, airconditioning efficiency, weather forecasts, status of other projects,etc.

With regard to the risk management decision acts 904 to be performed bythe RME, the RME may map information input during one or more of theinput acts 902 to the information output during the output acts 904.Acts performed by the RME may begin during act 917 and may include riskmanagement decision acts.

During act 917, the RME may identify information related to one or moreof the retrofit variables 909 and determine a quantization level ofthese variables. For example, in accordance with some embodiments, aretrofit variable may be explored over a continuously variable range(e.g. LPD 0.5 to 1.0 W/SF), to constrain the optimization problem to areasonable computational load, LPD may be quantized to 0.5 W/SF, 0.8W/SF, and 1.0 W/SF. After completing act 917, the process may continueto act 919.

During act 919, the process may perform simulations and/or numericalstudies with respect to retrofit variables. In accordance with someembodiments of the present system, all combinations of quantizedretrofit variables may be simulated to generate data to relate outputmetrics (such as energy consumption) to the retrofit variables. Thesimulations and/or numerical studies may then yield an output ofnumerical values related to building metrics such as one or more ofenergy consumption, pollution emission levels, occupant comfort levels,etc., for the building. After completing act 919, the process maycontinue to act 921.

During act 921, the process may perform a machine learning act. Themachine learning act may employ a learning technique to learn (e.g.,generate) a model relating one or more of the retrofit variables toperformance. In accordance with some embodiments of the present system,supervised learning algorithms may be used on the data generated duringact 919 to construct generalized functions to map the retrofit variablesto the output metrics. After completing act 921, the process maycontinue to act 923.

During act 923, the process may form a meta model which may includeinformation related to recommended retrofit variables. In accordancewith some embodiments of the present system, the meta model may be themodel (relating performance metrics as functions of retrofit variables)resulting from the machine learning act. After completing act 923, theprocess may continue to act 925.

During act 925, the process may assess the meta model generated duringact 921 to determine whether the model is good or bad. The process mayperform this act using any suitable method. In accordance with someembodiments of the present system, historical data or data sets withknown correlations between inputs and outputs may be used to test themeta model's ability to predict outputs based on inputs. Accordingly, ifit is determined that the model is good, the process may continue to act927. However, if it is determined that the model is bad, the process mayrepeat act 917. In accordance with some embodiments of the presentsystem, when repeating act 917 some information may be changed. Forexample, a retrofit variable, such as LPD may be re-quantized from thequantization levels of the previous example (e.g., 0.5 W/SF, 0.8 W/SF,and 1.0 W/SF) to 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 W/SF.

During act 927, the process may store the metal model generated duringact 921 as the historical data 915 for later use. The meta model mayinclude information such as equations relating retrofit variables toperformance metrics. After completing act 927, the process may continueto act 929.

During act 929, the process may optimize the retrofit variables tooptimize the meta model (e.g., the learned model). To optimize the metamodel, the process may use the information related to uncertaintyinformation (see, uncertainties obtained during act 905) to refine themeta model and for performance optimization using the refined metamodel. The optimization may be performed with respect to client goalsand/or provider goals, using techniques including, but not limited to,linear programming, nonlinear programming, coordinate descent algorithmsand Pareto frontier characterization. The optimization may formrecommended retrofit variable information as will be described belowwith respect to act 931 and which may include information related toexpected risk and return estimations for one or more of a client and aprovider. After completing act 929, the process may continue to act 931.

During act 931, the process may separate the recommended retrofitvariable information into information that is related to expected riskand return estimations for the client 933 and for the provider 935,respectively. The information related to expected risk and returnestimations may be related to financial, environmental and/or otherbuilding performance metrics. In accordance with some embodiments of thepresent system, each retrofit solution may be presented (e.g., to a userby rendering the information) showing performance metrics as probabilitydensity functions, which can then be further analyzed to provide rangesof returns for given ranges of probabilities. For example, the processmay render (e.g. on a display of the client), store (e.g., in a memory),and/or transmit (e.g., to a selected address of the client such as anemail address, a social networking address, etc.), this information asmay be desired. After completing acts 933, 935, the process may continueto act 937 during which a portfolio optimization process may beperformed to optimize the retrofit variable information with otherretrofit variable information (e.g., of other buildings) obtained fromthe historical data 915. For example, the provider may be involved inmany renovations projects simultaneously. Accordingly, by combiningcertain retrofit projects for a plurality of buildings together, theprovider may be able to diversify and/or reduce the overall risk/costsassociated with the portfolio. Therefore, it may be advantageous for theprovider to optimize over the retrofit projects using the historicaldata 915. Further, the process may store information related to thecurrent project (e.g., for the current building) such as the recommendedretrofit information (or parts thereof) as a part of the historical data915.

EXAMPLE

An example of a process performed by the RME 1122 will now be describedwith reference to FIG. 11. Acts of the RME 1122 may be performed, atleast in part, by a simulation tool such as a multi-platform softwareapplication known as EnergyPlus™ (e.g., v 7-1-0) operating in accordancewith embodiments of the present system. The RME 1122 may obtaininformation related to, for example, building properties (see, act 903),local weather (see, act 905), and retrofit variables (see, act 909) asinputs and may generate a meta model such as a metal model 1124 whichmay include recommendation information which may include recommendedretrofit variables as an output. The recommended retrofit variables maypredict future risks and returns to a client and/or a provider.

The information related to the required retrofit variables (see, act909) may include information such as daylight hours 1102, daylightcontrol (e.g., dimming control) profile 1112, occupancy sensors 1114, aschedule of the building (e.g., a lighting schedule e.g., on/off/dim)1120, lighting fixture composition 1110, temperature set points, 1108,etc. The process 1100 may employ any suitable method such as byemploying simulation methods performed by any suitable application suchas SPOT™ and/or CalcZone™ and determine the LPD information 1118 basedupon the lighting fixture composition 1110 and daylight control (dimmingcontrol) profile 1112. Then, the RME 1122 may process the heatinformation 1116, the LPD information 1118, and/or the scheduleinformation 1120 as input information. Then, the RME 1122 may processthe input information and output the meta model 1124 based at least inpart upon the input information. The meta model 1124 may includeinformation related to recommended retrofit variables. In accordancewith some embodiments of the present system, a simulation tool may beused as part of the RME. For example, all combinations of quantizedinputs may be simulated using Energy Plus to determine output data. Theinput and output data may then be used in the machine learning step togenerate the meta model. For example, a simulation tool such as EnergyPlus may be given quantized inputs to generate a simulated output andthese results from Energy Plus may be used by the RME to generate themeta model.

With the above-described inputs, the EnergyPlus™ application of the RME1122 operating in accordance with embodiments of the present system, mayform one or more simulation models of the building's performance. Theoutput from the simulation application, such as the EnergyPlus™application, may include the meta model 1124 which may includeinformation related to recommended retrofit variables and may provide adescription of various building performance metrics predicted for aselected time period such as a year. However, in yet other embodiments,other time periods (e.g., 0.5, 1, 5, etc., years) may be selected. Themeta model 1124 may further include information such as informationrelated to hourly human occupancy information, comfort level prediction,electricity consumption (hourly, cumulative, etc.), natural gasconsumption (hourly, cumulative, etc.), and emission levels. With regardto the comfort level this may be expressed as a percentage of occupantswhich do not find the environmental conditions (e.g., temperature,humidity, etc.) to be comfortable.

The RME 1122 may then learn from the simulated results another modelthat includes information which can predict future building performanceunder various weather conditions and retrofit variables. The RME 1122may further take into account retrofit project present cost (e.g., whichmay be estimated using any suitable simulation software application suchas CostWorks™), future energy price forecast (which is an uncertainvariable), and may optimize these variables with respect to a certainobject function over the course of a future time period such as the next10 years. Examples include net present value for example with a discountfactor 10%, while guaranteeing a certain thermal comfort levelrequirement, as follows,min NPV(Elec+Gas+installation),subject μ+0.5σ<=25% (PPD).

where min NPV is the minimum Net Present Value, μ is the mean variationof user satisfaction over time, σ is the standard deviation of usersatisfaction over time and PPD is the Percentage of People Dissatisfied.

The comfort level requirement may be different depending upon theapplication, for instance, a hospital should have a higher comfort levelrequirement than another building such as a warehouse. The comfort levelrequirement may be considered an input restraint and may be representedas a percentage of occupants of the building that will be dissatisfiedwith their thermal environment. For example, the above-describedequation uses a 25% PPD, which means at most 25% people will bedissatisfied with their thermal environment for most of the time(mean+0.5 standard deviation captures about 70% of all time incidences).Other input constraints (e.g., objective functions) may include anexpected payback period of the retrofit project, an expected internalrate of return (IRR), a variance of energy consumption, where theuncertainty comes from both the weather and the energy prices (e.g., ofelectricity and natural gas) and a ratio of an expected value of thebuilding and the variance of energy consumption. Other constraints mayinclude a reduction in emission level, carbon footprint and/or areduction of electricity consumption.

Test Building

A medium office building with 3 stories and 15 zones was tested inChicago, USA, in accordance with embodiments of the present system. Theprocess obtained energy prices from an energy model forum and calculatedrisks and returns for a client for a future time period. The calculatedrisks and returns were included in a model of the building's performancegenerated in accordance with embodiments of the present system andincluded a meta model output by an RME in accordance with embodiments ofthe present system.

FIG. 12A shows a graph of risk factors of a client generated inaccordance with embodiments of the present system; FIG. 12B shows agraph illustrating energy savings of solutions generated in accordancewith embodiments of the present system. With regard to FIG. 12A, thisgraph illustrates that total savings is not a single number, but adistribution represented as a probability density function. FIG. 12Billustrates a graph in which a comparison of two solution probabilitydensity functions, where solution one represents less savings but highercertainty (i.e., less risk), and solution two represents a greater rangeof savings (higher possible savings than solution one) but with lesscertainty (i.e., more risk).

FIG. 13 shows a graph 1300 of retrofitted office operating cost againstprice scenarios generated in accordance with embodiments of the presentsystem. Referring to graphs 1200A, 1200B, and 1300, total savings to theclient together with the projected probability of how likely thesesavings amounts might occur is shown. Further, graph 1300 shows asignificant savings is expected for an optimization of the building ascompared to the current configuration of the building. Moreparticularly, graph 1300 shows a plot of expected net present value (foroperating costs) calculations for the building for 30 different (random)weather scenarios generated by the process in accordance withembodiments of the present system.

The graph 1300 was determined based upon an office schedule (e.g., for abuilding categorized as an office building) and shows net present valueof the operating costs for first and second recommended optimal retrofitprojects 1302 and 1304, respectively, (optimized without or withdaylight control, respectively) versus a current configuration 1306 forthe building.

The results shown in the graph 1300 show that in accordance withembodiments of the present system, clear financial incentives may beprovided that are robust with respect to future weather realizations.For example, with respect to the predicted weather, in some embodiments,the process may take into account expected global warming over, forexample, a 5 year window and provide expected energy costs for abuilding in accordance with, at least, the expected global warming. Inyet other embodiments, the process may predict other costs such as grosscosts, net costs, net operating income (NOI) etc., for a future timeperiod based upon deterministic and/or stochastic risk factors.

Although financial aspects (e.g., net present value of the operatingcost) of the recommended retrofit project are shown, in yet otherembodiments, it is envisioned that results for environmental impact forthe building based upon the same or similar inputs may be determined.For example, it is envisioned that the process may compute a building'scontribution to greenhouse gases due to energy use.

FIG. 14 shows a portion of a system 1400 in accordance with anembodiment of the present system. For example, a portion of the presentsystem 1400 may include a processor 1410 (e.g., a controller)operationally coupled to a memory 1420, a user interface 1430, sensors1440, and a user input device 1470. The memory 1420 may be any type ofdevice for storing application data as well as other data related to thedescribed operation. The application data and other data are received bythe processor 1410 for configuring (e.g., programming) the processor1410 to perform operation acts in accordance with the present system.The processor 1410 so configured becomes a special purpose machineparticularly suited for performing in accordance with embodiments of thepresent system. The sensors may include, for example, occupancy sensors,sunlight sensors, humidity sensors, temperature sensors, barometricsensors, HVAC sensors, light sensors, switch status sensors (e.g., whichprovide information indicative of whether the switch is on, off and/ordimmed), weather sensors (e.g., temperature, humidity, barometricpressure, etc.).

The operation acts may include configuring the system 1400 by, forexample, configuring the processor 1410 to obtain information from userinputs, the sensors 1440, and/or the memory 1420 and processing thisinformation in accordance with embodiments of the present system toobtain information related to energy use and which may form at leastpart of information in accordance with embodiments of the presentsystem. The user input portion 1470 may include a keyboard, a mouse, atrackball and/or other device, including touch-sensitive displays, whichmay be stand alone or be a part of a system, such as part of a personalcomputer, a notebook computer, a netbook, a tablet, a smart phone, apersonal digital assistant (PDA), a mobile phone, and/or other devicefor communicating with the processor 1410 via any operable link. Theuser input portion 1470 may be operable for interacting with theprocessor 1410 including enabling interaction within a UI as describedherein. Clearly the processor 1410, the memory 1420, the UI 1430 and/oruser input device 1470 may all or partly be a portion of a computersystem or other device such as a client and/or server as describedherein.

Operation acts may include requesting, providing, and/or rendering ofinformation such as, for example, information related to energy use of abuilding. The processor 1410 may render the information on the UI 1430such as on a display of the system. The sensors may include suitablesensors to provide desired sensor information to the processor 1410 forfurther processing in accordance with embodiments of the present system.

The methods of the present system are particularly suited to be carriedout by processor programmed by a computer software program, such programcontaining modules corresponding to one or more of the individual stepsor acts described and/or envisioned by the present system.

The processor 1410 is operable for providing control signals and/orperforming operations in response to input signals from the user inputdevice 1470 as well as in response to other devices of a network andexecuting instructions stored in the memory 1420. For example, theprocessors 1410 may obtain feedback information from the sensors 1440and may process this information to determine energy conservationinformation. The processor 1410 may include one or more of amicroprocessor, an application-specific or general-use integratedcircuit(s), a logic device, etc. Further, the processor 1410 may be adedicated processor for performing in accordance with the present systemor may be a general-purpose processor wherein only one of many functionsoperates for performing in accordance with the present system. Theprocessor 1410 may operate utilizing a program portion, multiple programsegments, or may be a hardware device utilizing a dedicated ormulti-purpose integrated circuit.

Embodiments of the present system may be compatible with conventionalsensors/loggers such as OccuSwitch™ Logger and the like and/orconventional simulation programs such as EnergyPlus™, ESP-r™, AutodeskGreen Building Studio™, Integrated Environmental Solutions VirtualEnvironment™ and the like. The simulation programs may be configured inaccordance with embodiments of the present system so as to be able toperform in accordance with one or more embodiments of the presentsystem.

Accordingly, by locating sensors in accordance with embodiments of thepresent system, building energy use may be determined accurately andwith sufficient granularity for any building.

Finally, the above-discussion is intended to be merely illustrative ofthe present system and should not be construed as limiting the appendedclaims to any particular embodiment or group of embodiments. Thus, whilethe present system has been described with reference to exemplaryembodiments, it should also be appreciated that numerous modificationsand alternative embodiments may be devised by those having ordinaryskill in the art without departing from the broader and intended spiritand scope of the present system as set forth in the claims that follow.In addition, the section headings included herein are intended tofacilitate a review but are not intended to limit the scope of thepresent system. Accordingly, the specification and drawings are to beregarded in an illustrative manner and are not intended to limit thescope of the appended claims.

The specification and drawings are to be regarded in an illustrativemanner and are not intended to limit the scope of the appended claims.

In interpreting the appended claims, it should be understood that:

a) the word “comprising” does not exclude the presence of other elementsor acts than those listed in a given claim;

b) the word “a” or “an” preceding an element does not exclude thepresence of a plurality of such elements;

c) any reference signs in the claims do not limit their scope;

d) several “means” may be represented by the same item or hardware orsoftware implemented structure or function;

e) any of the disclosed elements may be comprised of hardware portions(e.g., including discrete and integrated electronic circuitry), softwareportions (e.g., computer programming), and any combination thereof;

f) hardware portions may be comprised of one or both of analog anddigital portions;

g) any of the disclosed devices or portions thereof may be combinedtogether or separated into further portions unless specifically statedotherwise;

h) no specific sequence of acts or steps is intended to be requiredunless specifically indicated; and

i) the term “plurality of” an element includes two or more of theclaimed element, and does not imply any particular range of number ofelements; that is, a plurality of elements may be as few as twoelements, and may include an immeasurable number of elements.

REFERENCES

References 1-5 listed below are incorporated herein by reference and arereferred to using reference numerals R1 through R5, respectively,throughout the specification. For example, R1 may make reference to thefirst reference (e.g., ecoinsight audit tool).

-   1. ecoinsight audit tool: available through the Internet at    ecoinsight.com.-   2. N. Djuric, V. Novakovic, J. Hoist and Z. Mitrovic, “Optimization    of energy consumption in buildings with hydronic heating systems    considering thermal comfort by use of computer-based tools”, Energy    and Buildings, 39, pp 471-477 (2007).-   3. M. Deru, E. Kozubal and P. Norton, “Walmart Experimental Store    Performance Stories”, NREL/CP-550-48295, ACEEE Summer Study (2010).-   4. B. Eisenhower, Z. O'Neill, S. Narayanan, V. A. Fonoberov and I.    Mezic, “A methodology for meta-model based optimization in building    energy models”, Energy and Buildings, 47, pp 292-301 (2012).-   5. S. Petersen and S. Svendsen, “Method for component-based    economical optimisation for use in design of new low-energy    buildings”, Renewable Energy, 38, pp 173-180 (2012).

The invention claimed is:
 1. An energy audit system for use inretrofitting energy consuming devices in an energy consuming space,comprising: at least one controller configured to: obtain attributeinformation (AI) for the energy consuming devices, the AI comprisinginformation related to a plurality of attributes and correspondingattribute choice information for each of the plurality of attributes;determine dependencies between each of the plurality of attributes toeach determine a dependency score for each of the plurality ofattributes based on the determined dependencies; determine a ranking ofeach of the attributes based upon the dependency score of each of theattributes; select a subset of the plurality of attributes based on theranking; determine an optimized workflow based upon the subset of theplurality of attributes; and in accordance with the optimized workflow,render a user interface (UI) for use in retrofitting at least one of thedevices to thereby obtain energy savings; wherein said retrofittingcomprises at least one of replacing a lighting fixture and replacing alighting control device.
 2. The system of claim 1, wherein thecontroller further is configured to obtain attribute choice informationselected by a user and which corresponds to at least one of the subsetof the plurality of attributes.
 3. The system of claim 1, wherein thecontroller is configured to determine the dependencies of each of theplurality of attributes to each other of the plurality of attributes byconstructing a dependency matrix.
 4. The system of claim 1, wherein thecontroller is configured to compare the dependency score of theplurality of attributes to a threshold value, wherein the subset of theplurality of attributes is selected as ones of the plurality ofattributes with a dependency score that meets or exceeds the thresholdvalue.
 5. The system of claim 4, wherein the controller is configured toautomatically populate attribute choice information corresponding to atleast one of the subset of the plurality of attributes that isdetermined to be a dependent attribute.
 6. A method of generating anenergy audit for use in retrofitting energy consuming devices in anenergy consuming space, the method performed by at least one controllerand comprising acts of: obtaining attribute information (AI) for theenergy consuming devices, the AI comprising information related to aplurality of attributes and corresponding attribute choice informationfor each of the plurality of attributes; determining dependenciesbetween each of the plurality of attributes to each other; determining adependency score for each of the plurality of attributes based on thedetermined dependencies; determining a ranking of each of the attributesbased upon the dependency score of each of the attributes; selecting asubset of the plurality of attributes based on the ranking; determiningan optimized workflow based upon the subset of the plurality ofattributes; and in accordance with the optimized workflow, rendering auser interface (UI) for use in retrofitting at least one of the devicesto thereby obtain energy savings; wherein said retrofitting comprises atleast one of replacing a lighting fixture and replacing a lightingcontrol device.
 7. The method of claim 6, further comprising an act ofobtaining attribute choice information selected by a user and whichcorresponds to at least one of the subset of the plurality ofattributes.
 8. The method of claim 6, further comprising an act ofdetermining the dependencies of each of the plurality of attributes toeach other of the plurality of attributes by constructing a dependencymatrix.
 9. The method of claim 6, further comprising an act of comparingthe dependency score of the plurality of attributes to a thresholdvalue, wherein the act of selecting the subset of the plurality ofattributes comprises an act of selecting ones of the plurality ofattributes with a dependency score that meets or exceeds the thresholdvalue.
 10. The method of claim 9, farther comprising an act ofautomatically populating attribute choice information corresponding toat least one of the subset of the plurality of attributes that isdetermined to be a dependent attribute.
 11. A computer program stored ona non-transitory computer readable memory medium, the computer programconfigured to generate information indicative of an energy audit for usein retrofitting energy consuming devices in an energy consuming space,the computer program comprising: a program portion configured to: obtainattribute information (AI) for the energy consuming devices, the AIcomprising information related to a plurality of attributes andcorresponding attribute choice information for each of the plurality ofattributes; determine dependencies between each of the plurality ofattributes to each other; determine a dependency score for each of theplurality of attributes based on the determined dependencies; determinea ranking of each of the attributes based upon a dependency score ofeach of the attributes; select a subset of the plurality of attributesbased on the ranking; determine an optimized workflow based upon thesubset of the plurality of attributes; and in accordance with theoptimized workflow, render a user interface (UI) on a display of thesystem for use in retrofitting at least one of the devices to therebyobtain energy savings; wherein said retrofitting comprises at least oneof replacing a lighting fixture and replacing a lighting control device.